Cone-beam CT (CBCT) is finding increased use in image-guided procedures, including orthopaedic surgeries such as spine fusion and total hip arthroplasty. Since intraoperative imaging is particularly likely to include surgical devices (e.g. tools, implants, or prostheses) within the tomographic field-of-view and these components have known composition, size, and shape, there is a unique opportunity to integrate such information in a re- construction approach. The investigators have developed a novel model-based approach called known- component reconstruction (KCR) that leverages known attenuation distributions, modeling an object comprised of known components (with unknown pose), as well as an unknown background anatomy. This is a new paradigm for incorporating prior object knowledge into a reconstruction framework where the algorithm jointly estimates both the background attenuation and the registers the known components. The technique is particularly well-suited to missing data and low signal-to-noise, as is common in interventional imaging due to metallic devices. Traditional reconstruction approaches are prone to severe metal streak artifacts (especially at low doses) with the poorest image quality in locations proximal to the device, which is often precisely the area of interest with the greatest image quality demands (e.g. visualization of nearby critical structures or interfaces of implants). Preliminary studies demonstrate that KCR is able to essentially eliminate artifacts associated with metal and allows for visualization of the object right up to the boundary of the tool or implant. We hypothesize tha an integrated system based on a generalized KCR framework with a library of known device components can provide artifact-free reconstructions in proximity to surgical implants, facilitatin high- precision device placement and dose reduction protocols in interventional CBCT. The following Specific Aims are proposed: 1.) Build a generalized analytic framework for KCR. Studies include development of a complete physics model for interventional CBCT, leveraging KCR's unique integration of component know- ledge, and adopting a deformable transformation model to allow for a broad class of inexactly known components (e.g., fixation rods in spine fusions that are deformed during a procedure to enforce a specific spine curvature). 2.) Create an integrated system for KCR. The development includes methods for generation of high- fidelity parameterized component models from CAD files or physical devices, computationally efficient algorithms and hardware, and tools for assessment of geometric accuracy in device placement from the component registration computed jointly in KCR. 3.) Evaluate KCR in pre-clinical experiments and simulated procedures. Work includes a systematic series of experiments using phantoms and cadavers with multiple components, deformable constructs, and conditions that stress the limits of noise, dose, object size, and implant size. Outcome measures will include quantitative imaging performance metrics, physician scoring, and registration error analysis, as well as the relation of these metrics to minimum-dose acquisition protocols.